Linganisha mbinu

	Njia ya Bayesian Synthetic Control	Uchambuzi wa Athari ya Kifahili
Nyanja	Uhitimisho wa Kisababishi	Uhitimisho wa Kisababishi
Familia	Regression model	Regression model
Mwaka wa asili	2015 (Bayesian formulation); 2003 (original SCM by Abadie & Gardeazabal)	2015
Mwanzilishi	Brodersen, Gallusser, Koehler, Remy & Scott; building on Abadie, Diamond & Hainmueller	Kay H. Brodersen, Fabian Gallusser, Jim Koehler, Nicolas Remy, Steven L. Scott (Google)
Aina	Bayesian causal inference / synthetic control	Bayesian causal inference / counterfactual forecasting
Chanzo asilia	Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274.	Brodersen, K. H., Gallusser, F., Koehler, J., Remy, N., & Scott, S. L. (2015). Inferring causal impact using Bayesian structural time-series models. Annals of Applied Statistics, 9(1), 247-274.
Majina mbadala	Bayesian SCM, Bayesian synthetic controls, probabilistic synthetic control, Bayesian SC	CausalImpact, BSTS causal inference, Bayesian causal impact, counterfactual time-series analysis
Zinazohusiana	5	5
Muhtasari	The Bayesian Synthetic Control Method estimates the causal effect of an intervention on a single treated unit by constructing a probabilistic counterfactual from a weighted combination of untreated donor units. Unlike the classical SCM, it places a prior distribution over the synthetic weights, yielding full posterior uncertainty intervals for the counterfactual trajectory and the treatment effect at each post-intervention time point.	Causal Impact Analysis, introduced by Brodersen et al. (2015) at Google, uses Bayesian structural time-series models to estimate what would have happened to an outcome had an intervention never occurred. By constructing a probabilistic counterfactual from pre-treatment data and control covariates, it quantifies point-in-time and cumulative treatment effects with full posterior uncertainty intervals.